# The World of Fast Moving Objects

arXiv: https://arxiv.org/abs/1611.07889 · HTML: https://mlsanity.com/paper/1611.07889/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos](https://mlsanity.com/paper/2111.14465/) (2021)
- [Tracking Small and Fast Moving Objects: A Benchmark](https://mlsanity.com/paper/2209.04284/) (2022)
- [Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects](https://mlsanity.com/paper/2106.08762/) (2021)
- [Finding Geometric Models by Clustering in the Consensus Space](https://mlsanity.com/paper/2103.13875/) (2021)
- [Benchmarking EfficientTAM on FMO datasets](https://mlsanity.com/paper/2509.06536/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion](https://mlsanity.com/paper/2306.14492/) (2023)
- [Retrieval Robust to Object Motion Blur](https://mlsanity.com/paper/2404.18025/) (2024)
- [Benchmarking SAM2-based Trackers on FMOX](https://mlsanity.com/paper/2512.09633/) (2025)
- [PoseStreamer: A Multi-modal Framework for 3D Tracking of Unseen Moving Objects](https://mlsanity.com/paper/2512.22979/) (2025)
- [Recovering 3D Shapes from Ultra-Fast Motion-Blurred Images](https://mlsanity.com/paper/2602.07860/) (2026)

## Abstract (arXiv)

The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semi-transparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal super-resolution and highlighting, are presented.